One more point. It is interesting to note that from 2004 - 2060 the difference between the amount of estuarine water in our base run and our revised SE run are negligible (i.e., most of the difference in mangrove area is accounted for my increases in fresh water habitats). It is in 2070 that we suddenly see over 1,000 ha more Estuarine water than we did in the base run, as though some sort of threshold is being crossed between 2060 and 2070.
We are not using the pre-processor here, but did make some alterations to the conceptual model as follows:
• Tidal Fresh Marsh – changed to 40 percent of salt elevation.
• Tidal Swamp - changed to 40 percent of salt elevation.
• Inland Fresh Marsh – changed to 50 percent of salt elevation.
• Irregularly Flooded Marsh – Due to the unique tidal regime that characterizes the estuary, traditional "Regularly flooded marsh" that are inundated and exposed on a daily basis do not exist as they do in other east coast estuaries. We allow this habitat type to persist to the mean tide level (i.e., 0 meters relative to MTL=0) at all sites.
I did not create maps for this run, but could send you the tabular results. Would that be helpful?
In testing the sensitivity of SLAMM to the salt elevation parameter I came across a situation that I'm having trouble understanding. At our tropical site, we had been using a salt elevation of 0.325 m above MTL and a GDT of 0.05 m. To test the sensitivity of the model to salt elevation, we reduced it by 25% to 0.244 and ran the model again to compare our results in 2100. As expected, as compared to the base run, we saw increases in most fresh habitats and much less mangrove creation, as fresh habitats were now allowed to persist to a lower elevation. However, the majority of the decrease in area of mangroves created was offset by a large increase in the amount of estuarine water created. I cannot understand how this would have happened. (It was not because more inland open water was turning into estuarine water, and it clearly appears to be an issue of area once converting to mangroves now instead converting to estuarine water).
Well, I thought that was my last question on this topic, but I've discovered another. What, if anything, is converting to Transitional Salt Marsh in a tropical system? I'm assuming that there is still some conversion to this category based upon my results. In sites that I know were recognized as tropical I still see some transitional marsh popping up. But in sites that were not recognized as tropical, significantly more is created. As a follow-up similar to my previous question, can we assume roughly that most of these transitional marshes in 2100 really ought to be mangroves?
Thank you Jonathan, this is very helpful. One final question: in a tropical system, is the parameter "Regularly Flooded Marsh Accretion Rate" then applied to Mangroves? It is not clear from the documentation.
Thanks for this description, I now see what happened. It looks like not all of our subsites met the "Tropical" threshold, despite the fact that they are all part of the same system. Do you think it would be appropriate, than, in explaining this situation, to essentially add the regularly flooded salt marsh in 2100 to the mangrove acreage, stating that all of those reg. flooded marshes should have converted to mangrove? Do the implications of this issue extend beyond this simple explanation?
As you know, I'm modeling a tropical site, and I'm trying to understand the conversions that are occurring with inundation and erosion. I attempted to go through the technical documentation to "Map out" the successive conversions that happen (e.g., using the table on p. 32 and information within the habitat category descriptions). However, I still have some holes. Most critically, I'm trying to understand what habitat type converts to Regularly flooded marsh in a tropical system. My understanding what that the "default" for a tropical system would be a conversion to mangrove when a fresh or dry habitat goes below the salt elevation. Could this just be my irregularly flooded marshes converting to regularly flooded? Is it the case in a tropical system that irregularly flooded marshes are never "created" (because the default is mangrove) and that regularly flooded marshes are only created as a result of inundation of existing irregularly flooded marshes?
I don't suppose anyone has attempted to draw out a "decision tree" with the conversions that you could share?
I've read in several places that the ideal change in a habitat type between the initial condition and time 0 would be less than 5% or so. I've got a situation here where the transition for all but one category is less than or just a hair over 5%. However, all of the land converting FROM those categories that are losing land area in T=0 are converting to mangroves, causing the difference in mangrove area from initial state to T=0 to be +115%. (Note that the conceptual model is fitting very well to our habitat types, with the lower bound of each category matching the 5th percentile fairly well). In a decision tree in which pretty much everything below salt elevation turns into mangrove, I'm not sure how this could be avoided. For example, even though we only lose 2% of our swamps to mangrove from initial to T=0, that is 105 ha, which is a lot of new mangroves in T=0 considering we only began with 600 ha of mangrove.
I'm not sure how concerned I should be about this. The total cover of mangrove in the initial state is only 1%, and the 115% increase in mangroves still leaves total mangrove coverage fairly low, at 2%. As you are aware, we've checked and double checked our parameterization. My next step would be to lower the elevations in the conceptual model for some of the fresh habitats... Thoughts?
I am using SLAMM to model an estuary that exists at the very northern range of mangrove habitat. The northern part of my study area can be mangrove dominated or salt marsh dominated depending on the occurrence and frequency of hard freezes. Thus, SLAMM's decision rule to convert almost every wetland habitat type into mangrove upon inundation leaves us with nearly all lands converted to mangrove by 2100 - a highly unlikely outcome. Is there any way to override the mangrove succession such that we can run a simulation that assumes this area to NOT be tropical (i.e., remove "mangroves" from the succession tree)? This would allow me to compare my original results to results from a scenario in which the area is salt marsh-dominant (which might simulate what the area would like like if several hard freezes occurred over the course of the simulation).
I'm trying to understand SLAMM's use and interpretation of erosion rates and have not found a clear explanation in the documentation. It appears to me from the technical documentation that erosion is modeled as "none", "Heavy" or "severe" depending on the fetch. Since we input the erosion rates for marshes, swamps and tidal flats specifically into the model, what does it mean for SLAMM to model severity of erosion? Does that mean it only uses some "portion" of the rate we gave it depending on the situation (e.g.., 100% for severe, 50% for heavy"), or does it change something else?
In simpler terms, my question is, does rising sea level affect the rate of erosion for these habitat types?
I cannot answer your question, unfortunately, but definitely have similar questions. Regarding your question about the VDATUM input and outputs, we were similarly confused. Since we are inputing an XYZ elevation raster, it seemed to us that counter to what has been suggested on the forum (MTL as input and NAVD as output) we are inputing NAVD and requesting MTL as the output. The only explanation we had for the suggestion of using MTL as the input and NAVD as the output is that others might have been simply putting into VDATUM one MTL value (i.e. one point) and using VDATUM to give them NAVD so they could calculate the correction factor for this point. The same does not seem to apply if you are actually inputting an elevation raster, but I'm anxious to hear what the moderators have to say....
We are having an issue with the elevation analysis where several of the "5th percentile" results are identical to one another. Specifically, inland fresh marsh, tidal fresh marsh, mangrove, inland open water, ad irreg. flooded marsh all have a value of 8.2744. Additionally, the "min" for inland fresh marsh and tidal fresh marsh are also 8.2744. I looked more closely at elevation analyses from previous runs at other subsites, and this issue actually came up once before. It's unclear why it only occurs some of the time.
I am wondering if there's possibly an issue in the elevation analysis code. I did consider the possibility that this issue was a result of error in our input files. But thinking conceptually, that doesn't seem possible. The inputs are processed separately from one another; the values within the habitat dataset and the elevation dataset wouldn't influence one another during the input creation. Even if there were errors in our elevation data or the habitat data, it still wouldn't lead to identical values for the 5th percentiles. To confirm, I did take the final input text files and convert them back to raster, and compared them to the original rasters from which we make the input text files, and they're identical.
Any thoughts on what might be going wrong here? I'd like to use the elevation analysis to see if we need to make any revision to the conceptual model ranges, but I'm not feeling confident at this point that I can rely on the current analysis for that purpose.
Thank you again, Jonathan, for working to help us clarify this parameter. I now have a quick follow-up question to ensure that we are using this parameter appropriately.
We are using a salt elevation of 0.12 meters NAVD88. We need to apply the conversion factor in order to translate this parameter into the Salt Elevation relative to MTL, which is what the model requests as an input. Our instinct says that we should be subtracting the conversion factor (in this case, -0.205) from the NAVD salt elevation to arrive at the value that SLAMM is looking for. So, 0.12 m - (-0.205) = 0.325.
Do you agree with this application of the conversion factor?